🔮 China’s on a different AI path
China is deploying fast, frugal, open-weighted models and wiring them into the economy.
Hi Azeem here,
The US-China AI rivalry has become the defining geopolitical lens through which many view the future of technology. And for good reason: it’s shaping export controls, industrial policy, and innovation trajectories on both sides. However, when a single narrative becomes dominant, it can overshadow more nuanced analysis.
That’s why I’m thrilled to hand the mic to
, one of the sharpest analysts tracking China’s evolving tech landscape. Grace brings a unique perspective to the table that moves beyond great-power competition to explore the structural, commercial, and cultural dynamics shaping China’s AI ecosystem. She’s covered Asian tech for global outlets including Fortune, CNBC and the Economist Intelligence Unit. Grace has also advised top Chinese firms like Alibaba, Tencent and Lenovo – and now writes independently at .Over to Grace!
China’s different path
By
On Wednesday, the White House published the AI Action Plan, a playbook for building an AI innovation ecosystem. It’s a bet on destiny that intelligence, once summoned, will reorder the world.
China isn’t chasing destiny. It’s deploying fast, frugal, open-weighted models and wiring them into the economy.
That pragmatism unnerves Washington. Chinese labs now ship foundation models faster and cheaper, and – crucially – they publish the weights. Silicon Valley reads this as “open the weights, kill the moats” – a threat to the revenues that depend on keeping the model layer proprietary.
Inside China, the logic is flipped. Once the models are treated as commodities, profit shifts to the application layer. Publishing the weights speeds that shift. Free forks and vertical fine-tunes multiply, each funnelling demand back to the originator.
However, this isn’t some grand strategy being directed by the governments on both sides; it is market-driven, shaped by three structural forces – chips, capital, and distribution – that make open‑weight releases the logical on‑ramp to value.
Deployment vs destiny
The real split is over where each country’s tech companies believe the profit will land. China bets on applications; America bets on the model itself. Palo Alto obsesses over model‑led destiny: ever‑bigger parameters, safety benchmarks, and a near‑cult-like obsession in the pursuit of AGI. As
recounts in Empire of AI, Sam Altman and his peers see AGI as a world‑changing force capable of solving humanity’s most significant challenges. The wager is long, deep‑pocketed and proprietary: pour venture billions into loss‑making models today; own the platform that reorganizes industries tomorrow.China’s approach is more pragmatic. Its origins are shaped by its hyper‑competitive consumer internet, which prizes deployment‑led productivity. Neither WeChat nor Douyin had a clear monetization strategy when they first launched. It is the mentality of Chinese internet players to capture market share first. By releasing model weights early, Chinese labs attract more developers and distributors, and if consumers become hooked, switching later becomes more costly.
Entrepreneurs then have the opportunity to utilize these models as free scaffolding. Taking the EV industry as an example, over twenty Chinese automakers, including BYD, Geely, and Great Wall Motors, have integrated DeepSeek into their in-car AI systems to enhance smart assistants and autonomous driving capabilities. In healthcare, it is said that nearly 100 hospitals across the country have now integrated DeepSeek for medical imaging analysis and clinical diagnosis support. Every new integration expands the model’s footprint, tightens switching costs, and shifts margins to the services sitting on top.
China’s so‑called “open‑source strategy” is a corporate pragmatism play, not a grand geopolitical scheme. But to see why pragmatism took this particular form, you need to see the three forces driving it – chips, capital, and distribution – turn openness into the default.
Chip scarcity
America has had export controls on China since October 2022, preventing Chinese model makers from accessing the latest Nvidia GPUs. Overnight, the brute‑force “scale‑to‑AGI” playbook – whether China wanted it or not – was off the menu. Chinese labs were left with a patchwork of last-gen Nvidia GPUs and local chips, at least one to two years behind the US hardware frontier.
Because chip access is capped, efficiency has become the main event. Chinese researchers have been focusing on extracting the most from their hardware. DeepSeek‑V3 delivered GPT-4o performance at 18x cheaper cost, while Moonshot’s Kimi K2 used MuonClip, which likely halved the FLOPs used during a training run. America’s chip curbs have unintentionally turned China’s models into performance‑per‑yuan champions. As many have said, “necessity is the mother of invention.” Now, Chinese models are so competitive that both local and international companies want to build on them.
Capital drought
The same efficiency drive also aligns with corporate reality. With venture money scarce and a lingering urge to prove they’re innovators – not copy‑cats – Chinese founders must signal value fast.
The more the models are used, the bigger their moats and their name. This is an essential characteristic of Chinese AI, stemming from the fact that China’s funding environment is scarce compared to the US. US venture capital plowed $100 billion into AI in the first half of 2025. Meanwhile, Chinese startups across all sectors raised barely $11 billion from VCs. Since China’s leading ride‑hailing app DiDi delisted from the NYSE in 2022, a regulatory storm has swept across the country’s internet sector. Some American funds pulled out of China, and even Chinese ones became skittish. The US government implemented a rule that disallowed US investors from investing in AI and chips. Chinese start-ups in AI now, compared to ten years ago during the internet era boom, raise capital only after they can demonstrate a live product and real usage figures.
Chinese models may be efficient, but DeepSeek is still estimated to have spent over $500 million in total R&D for R1. Luckily, they were self-funded by hedge fund billionaire Liang Wengfeng. But how do the other startups get funding? If you look at the “four AI tigers” – Baichuan AI, Zhipu AI, Moonshot AI, and MiniMax – most had to secure backing from the BBAT quartet. For Baichuan and Zhipu, releasing strong open-weight checkpoints helped persuade Alibaba/Tencent that the teams could ship and attract a developer ecosystem, unlocking nine-figure cheques only after the code was dropped.
Distribution choke-points
Once a model maker has a reputation, what keeps them open-source?